most citedStochastic and incremental subgradient methods for convex optimization on Hadamard spaces

1 citations · 1 across the 6 of their papers we have counts for

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6 papers

math.OC2024★ 1 cited

Stochastic and incremental subgradient methods for convex optimization on Hadamard spaces

Ariel Goodwin, Adrian S. Lewis, Genaro López-Acedo +1

As a foundation for optimization, convexity is useful beyond the classical settings of Euclidean and Hilbert space. The broader arena of nonpositively curved metric spaces, which i…

math.OC2024

Local geometry of feasible regions via smooth paths

Adrian S. Lewis, Adriana Nicolae, Tonghua Tian

Variational analysis presents a unified theory encompassing in particular both smoothness and convexity. In a Euclidean space, convex sets and smooth manifolds both have straightfo…

math.OC2024

Recognizing weighted means in geodesic spaces

Ariel Goodwin, Adrian S. Lewis, Genaro Lopez-Acedo +1

Geodesic metric spaces support a variety of averaging constructions for given finite sets. Computing such averages has generated extensive interest in diverse disciplines. Here we…

math.OC2024

Convex optimization on CAT(0) cubical complexes

Ariel Goodwin, Adrian S. Lewis, Genaro Lopez-Acedo +1

We consider geodesically convex optimization problems involving distances to a finite set of points in a CAT(0) cubical complex. Examples include the minimum enclosing ball pro…

math.OC2024

Horoballs and the subgradient method

Adrian S. Lewis, Genaro Lopez-Acedo, Adriana Nicolae

To explore convex optimization on Hadamard spaces, we consider an iteration in the style of a subgradient algorithm. Traditionally, such methods assume that the underlying spaces a…

cs.CR2023

OpenBSD formal driver verification with SeL4

Adriana Nicolae, Paul Irofti, Ioana Leustean

The seL4 microkernel is currently the only kernel that has been fully formally verified. In general, the increased interest in ensuring the security of a kernel's code results from…